ArticleIEEE transactions on pattern analysis and machine intelligence2017
Selective Transfer Machine for Personalized Facial Expression Analysis.
Article in IEEE transactions on pattern analysis and machine intelligence, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- RS-STGCN: Regional-Synergy Spatio-Temporal Graph Convolutional Network for emotion recognition.Frontiers in neuroscience · 2025Article
- A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face.Entropy (Basel, Switzerland) · 2023Review
- Subject-Independent EEG Emotion Recognition Based on Genetically Optimized Projection Dictionary Pair Learning.Brain sciences · 2023Article
- Deploying deep learning models on unseen medical imaging using adversarial domain adaptation.PloS one · 2022Article
- Multi-Model Adaptation Learning With Possibilistic Clustering Assumption for EEG-Based Emotion Recognition.Frontiers in neuroscience · 2022Article
- Robust Latent Multi-Source Adaptation for Encephalogram-Based Emotion Recognition.Frontiers in neuroscience · 2022Article
- TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain.BMC bioinformatics · 2021Article
- Multi-Source Co-adaptation for EEG-Based Emotion Recognition by Mining Correlation Information.Frontiers in neuroscience · 2021Article
- Possibilistic Clustering-Promoting Semi-Supervised Learning for EEG-Based Emotion Recognition.Frontiers in neuroscience · 2021Article
- Hierarchical Attention-Based Multimodal Fusion Network for Video Emotion Recognition.Computational intelligence and neuroscience · 2021Article
- Visual Scene-Aware Hybrid and Multi-Modal Feature Aggregation for Facial Expression Recognition.Sensors (Basel, Switzerland) · 2020Article
- Crossing Domains for AU Coding: Perspectives, Approaches, and Measures.IEEE transactions on biometrics, behavior, and identity science · 2020Article
- Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements.Psychological science in the public interest : a journal of the American Psychological Society · 2019Review
- Cross-domain AU Detection: Domains, Learning Approaches, and Measures.Proceedings of the ... International Conference on Automatic Face and Gesture Recognition. IEEE International Conference on Automatic Face & Gesture Recognition · 2019Article
- Learning Domain-Independent Deep Representations by Mutual Information Minimization.Computational intelligence and neuroscience · 2019Article
- FACSCaps: Pose-Independent Facial Action Coding with Capsules.Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops · 2018Article
- Closed Loop Deep Brain Stimulation for PTSD, Addiction, and Disorders of Affective Facial Interpretation: Review and Discussion of Potential Biomarkers and Stimulation Paradigms.Frontiers in neuroscience · 2018Review
- Automatic Action Unit Detection in Infants Using Convolutional Neural Network.International Conference on Affective Computing and Intelligent Interaction and workshops : [proceedings]. ACII (Conference) · 2017Article
- A Branch-and-Bound Framework for Unsupervised Common Event Discovery.International journal of computer vision · 2017Article
- Sayette Group Formation Task (GFT) Spontaneous Facial Expression Database.Proceedings of the ... International Conference on Automatic Face and Gesture Recognition. IEEE International Conference on Automatic Face & Gesture RecognitionArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Automatic facial action unit (AU) and expression detection from videos is a long-standing problem. The problem is challenging in part because classifiers must generalize to previously unknown subjects that differ markedly in behavior and facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) from those on which the classifiers are trained. While some progress has been achieved through improvements in choices of features and classifiers, the challenge occasioned by individual differences among people remains. Person-specific classifiers would be a possible solution but for a paucity of training data. Sufficient training data for person-specific classifiers typically is unavailable. This paper addresses the problem of how to personalize a generic classifier without additional labels from the test subject. We propose a transductive learning method, which we refer to as a Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific mismatches. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. We compared STM to both generic classifiers and cross-domain learning methods on four benchmarks: CK+ [44], GEMEP-FERA [67], RUFACS [4] and GFT [57]. STM outperformed generic classifiers in all.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.